Highest Mortality Rate
Country: Somalia;Year 1990 Value: 179.0;Year 2024 Value: 108.0;Relative Change: -40.0%
Lowest Mortality Rate
Country: Finland;Year 1990 Value: 6.73;Year 2024 Value: 1.38;Relative Change: -79.0%
Highest GDP per Capita
Country: Monaco;Year 1990 Value: 106000.0;Year 2024 Value: 222000.0;Relative Change: 109.0%
Lowest GDP per Capita
Country: South Sudan;Year 1990 Value: 1540.0;Year 2024 Value: 363.0;Relative Change: -76.0%
Highest Population Size
Country: India;Year 1990 Value: 865000000.0;Year 2024 Value: 1450000000.0;Relative Change: 68.0%
Highest Population Size
Country: Tuvalu;Year 1990 Value: 8800.0;Year 2024 Value: 9650.0;Relative Change: 10.0%
| country | 1990 | 2024 | Absolute Change | Relative Change |
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| country | 1990 | 2024 | Absolute Change | Relative Change |
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| country | 1990 | 2024 | Absolute Change | Relative Change |
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This dashboard presents an interactive exploration of global and regional trends in child mortality, GDP per capita, and population size, with a special focus on Central America and the Dominican Republic. The analysis draws on data from 1990, 2002, and 2024 to visualize changes over time and examine the relationship between economic conditions and child health outcomes.
The dashboard includes:
Data Tables: For each country, a summary table shows child mortality rates in 1990 and 2024, the absolute change, and the relative change. This allows users to quickly assess progress over time.
Bar Charts: Comparative bar charts display child mortality and GDP per capita for 1990 and 2024, highlighting regional trends in Central America and the Dominican Republic.
Global Maps: Thematic maps provide a worldwide perspective on the distribution of child mortality, GDP per capita, and population size in 1990 and 2024.
Scatter Plots: Visualizations for the years 1990, 2002, and 2024 reveal the relationship between child mortality and GDP per capita across countries in Central America and the Dominican Republic, offering insights into how economic development correlates with improvements in child health.
Overall, this project demonstrates how data visualization tools in Python can be used to effectively communicate important global health and economic trends. It provides an accessible and engaging way to explore complex data and supports evidence-based understanding of development progress in the region.
All the data frames were obtained form GAPMINDER (https://www.gapminder.org/data/)